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A new framework for metaheuristic-based frequent itemset mining

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Abstract

This paper proposes a novel framework for metaheuristic-based Frequent Itemset Mining (FIM), which considers intrinsic features of the FIM problem. The framework, called META-GD, can be used to steer any metaheuristics-based FIM approach. Without loss of generality, three metaheuristics are considered in this paper, namely the genetic algorithm (GA), particle swarm optimization (PSO), and bee swarm optimization (BSO). This allows to derive three approaches, named GA-GD, PSO-GD, and BSO-GD, respectively. An extensive experimental evaluation on medium and large database instances reveal that PSO-GD outperforms state-of-the-art metaheuristic-based approaches in terms of runtime and solution quality.

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Notes

  1. Available at the KEEL repository https://archive.ics.uci.edu/ml/datasets.html, http://fimi.ua.ac.be/data/ and http://sci2s.ugr.es/keel/datasets.php, respectively.

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Authors and Affiliations

  1. IMADA, Computer Science Department, Southern Denmark University, Odense, Denmark

    Youcef Djenouri

  2. DTISI, CERIST Research Center, Algiers, 16030, Algeria

    Djamel Djenouri

  3. RIMA, USTHB, Algiers, Algeria

    Asma Belhadi

  4. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  5. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

Authors
  1. Youcef Djenouri
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  2. Djamel Djenouri
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  3. Asma Belhadi
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  4. Philippe Fournier-Viger
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  5. Jerry Chun-Wei Lin
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Correspondence to Youcef Djenouri.

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Djenouri, Y., Djenouri, D., Belhadi, A. et al. A new framework for metaheuristic-based frequent itemset mining. Appl Intell 48, 4775–4791 (2018). https://doi.org/10.1007/s10489-018-1245-8

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